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Nozari, Sajad, Dehghani, Lila, Chabok, Razieh, Moloudpour, Behrooz, Moradi Vastegani, Zahar, Moalemi, Somayeh, Mousavi, Masoumeh Sadat. (1400). Google Trend as an Early Warning System for Corona Outbreak Investigation in Iran. سامانه مدیریت نشریات علمی, 9(1), 16-21. doi: 10.30476/jhmi.2022.94812.1122
Sajad Nozari; Lila Dehghani; Razieh Chabok; Behrooz Moloudpour; Zahar Moradi Vastegani; Somayeh Moalemi; Masoumeh Sadat Mousavi. "Google Trend as an Early Warning System for Corona Outbreak Investigation in Iran". سامانه مدیریت نشریات علمی, 9, 1, 1400, 16-21. doi: 10.30476/jhmi.2022.94812.1122
Nozari, Sajad, Dehghani, Lila, Chabok, Razieh, Moloudpour, Behrooz, Moradi Vastegani, Zahar, Moalemi, Somayeh, Mousavi, Masoumeh Sadat. (1400). 'Google Trend as an Early Warning System for Corona Outbreak Investigation in Iran', سامانه مدیریت نشریات علمی, 9(1), pp. 16-21. doi: 10.30476/jhmi.2022.94812.1122
Nozari, Sajad, Dehghani, Lila, Chabok, Razieh, Moloudpour, Behrooz, Moradi Vastegani, Zahar, Moalemi, Somayeh, Mousavi, Masoumeh Sadat. Google Trend as an Early Warning System for Corona Outbreak Investigation in Iran. سامانه مدیریت نشریات علمی, 1400; 9(1): 16-21. doi: 10.30476/jhmi.2022.94812.1122

Google Trend as an Early Warning System for Corona Outbreak Investigation in Iran

Health Management & Information Science
دوره 9، شماره 1، خرداد 2022، صفحه 16-21    اصل مقاله (1.08 M)
نوع مقاله: Original Article
شناسه دیجیتال (DOI): 10.30476/jhmi.2022.94812.1122
نویسندگان
Sajad Nozari1؛ Lila Dehghani2؛ Razieh Chabok3؛ Behrooz Moloudpour4؛ Zahar Moradi Vastegani5؛ Somayeh Moalemi6؛ Masoumeh Sadat Mousavi* 7
1Assistant professor, Department of Epidemiology and Biostatistics, School of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran
2Department of Public Health Behbahan Faculty of Medical Sciences, Behbahan, Iran
3Student Research Committee, School of Nursing and Midwifery, Sabzevar University of Medical Sciences, Sabzevar, Iran
4Student Research Committee, School of Management and Medical Informatics, Tabriz University of Medical Sciences, Tabriz, Iran
5Alimentary Tract Research Center, Imam Khomeini Hospital Clinical Research Development Unit, Jundishapur University of Medical Sciences, Ahvaz, Iran
6School of Management and Medical Informatics, Shiraz University of Medical Sciences, Shiraz, Iran
7Assistant professor, Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran -Assistant professor, Department of Epidemiology and Biostatistics, School of Public Health, Shahrekord University of Medical Sciences, Shahrekord, Iran
چکیده
Introduction: Digital epidemiology is introduced as a major aspect of epidemiology; its
sources are digital data and it uses spaces such as Google, YouTube and Twitter as databases.
In the recent Covid-19 pandemic, the use of digital epidemiology, as an early warning system,
has been considered. This study aimed to investigate the context of Google Trend as an early
warning system in the study of coronavirus outbreaks in Iran.
Methods: The coronavirus epidemic in Iran started on February 24, 2020, and with some
differences to consider the rumors in the community, we consider the date before the
announcement of all by February 16, 2020 until November 16, 2021. We searched using
keywords related to symptoms such as “fever”, “cough” and “sore throat” and the keyword
“corona symptoms”; information was extracted and entered in Microsoft Excel and the
keyword chart was drawn according to the date of each wave. Spearman correlation test was
performed to find the correlation between keywords in SPSS version 18.
Results: The trend chart of the keywords “fever”, “cough” and “sore throat” and the keyword
“corona symptoms” in different waves of coronavirus in Iran showed an increase in keyword
searches before the onset of the corona epidemic wave. Spearman correlation coefficient
between sore throat and fever was 0.645, sore throat and cough 0.775, sore throat and corona
symptoms 0.684, between fever and cough keywords 0.435, fever and corona symptoms 0.779
and between keyword cough and corona symptoms 0.503. In all these coefficients, the level of
error of the first type was 0.05 significant (P<0.001)
Conclusion: Google Trend, a digital epidemiology tool, can be used as an effective early
warning system to control the corona pandemic, and this field of epidemiological knowledge
with all its limitations needs further research.

کلیدواژه‌ها
Google trend؛ Early warning system؛ outbreak investigation؛ Digital epidemiology؛ Iran؛ Covid 19
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